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Point processes on the complex plane with applications
(University of Missouri--Columbia, 2019)
[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] A point process is a random collection of points from a certain space, and point process models are widely used in areas dealing with spatial data. ...
Alternative learning strategies for spatio-temporal processes of complex animal behavior
(University of Missouri--Columbia, 2020)
The estimation of spatio-temporal dynamics of animal behavior processes is complicated by nonlinear interactions. Alternative learning methods such as machine learning, deep learning, and reinforcement learning have proven ...
Modeling gibbs point processes through basic function decompositions
(University of Missouri--Columbia, 2019)
[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] We consider non-homogeneous pairwise interaction point process models, where the global and local effect functions are modeled using basis function ...
Variable selection for interval-censored and functional survival data
(University of Missouri--Columbia, 2022)
Interval-censored data are a type of failure time data that is only known to belong to a time interval but cannot be observed precisely. Note that interval-censoring is often encountered in medical or health studies with ...
Variable selection for interval-censored failure time data
(University of Missouri--Columbia, 2019)
[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Variable selection is a commonly asked question and various traditional variable selection methods have been developed, including forward, backward and ...
Bayesian non-parametric methods for benefit-risk assessment and massive multiple-domain data
(University of Missouri--Columbia, 2019)
[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] The development of systematic and structured approaches to assess benefit-risk of medical products is a major challenge for regulatory decision makers. ...
Full Bayesian models for paired RNA-seq data and Bayesian equivalence test
(University of Missouri--Columbia, 2018)
[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] "In my doctorate research, I have developed Bayesian models to analyze the paired RNAseq data for different types of design. The developed methods are ...
Semiparametric methods for regression analysis of panel count data and mixed panel count data
(University of Missouri--Columbia, 2017)
[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Recurrent event data and panel count data are two common types of data that have been studied extensively in event history studies in literature. By ...
A Bayesian classification framework with label corrections
(University of Missouri--Columbia, 2014)
The use of unlabeled data is very important for regression and classification analysis in many cases. However, the data may have an extra layer of complexity with some wrongly labelled data points. The traditional ...
Bayesian analysis of fMRI data and RNA-Seq time course experiment data
(University of Missouri--Columbia, 2015)
The present dissertation contains two parts. In the first part, we develop a new Bayesian analysis of functional MRI data. We propose a novel triple gamma Hemodynamic Response Function (HRF) including the component to ...
Bayesian partition model for identifying hypo- and hyper- methylation
(University of Missouri--Columbia, 2017)
[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] This dissertation introduces MethyBayes, a full Bayesian partition model for identifying hypo- and hypermethylated loci. The main interest of study on ...
Semiparametric analysis of failure time data with complex structures /
(University of Missouri--Columbia, 2016)
[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Failure time data arise in many fields including biomedical studies and industrial life testing. Right-censored failure time data are often observed ...
Regression analysis of interval-censored failure time data with non proportional hazards models
(University of Missouri--Columbia, 2018)
[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Interval-censored failure time data arises when the failure time of interest is known only to lie within an interval or window instead of being observed ...
Bayesian hierarchical modeling of colorectal and breast cancer data in Missouri
(University of Missouri--Columbia, 2018)
Data on cancer in the United States is collected through cancer registries. The Missouri Cancer Registry and Research Center (MCR-ARC) maintains a statewide cancer surveillance system and participate in research in support ...
Some topics in multi-regional clinical trials and meta-analysis using Bayesian models
(University of Missouri--Columbia, 2017)
[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] The dissertation consists of two distinct research topics. One is about sample size determination in Multi-Regional Clinical Trials (MRCTs), the other ...
Objective Bayesian analysis of the 2 x 2 contingency table and the negative binomial distribution
(University of Missouri--Columbia, 2018)
In Bayesian analysis, the “objective” Bayesian approach seeks to select a prior distribution not by using (often subjective) scientific belief or by mathematical convenience, but rather by deriving it under a pre-specified ...
Functional data analysis : children's mathematical development
(University of Missouri--Columbia, 2016)
[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Bailey et al. (2014) suggested that children's mathematical development is related more to trait characteristics than to prior mathematical development. ...
Nonlocal priors for Bayesian variable selection in generalized linear models and generalized linear mixed models and their applications in biology data
(University of Missouri--Columbia, 2016)
A crucial problem in building a generalized linear model (GLM) or a generalized linear mixed model (GLMM) is to identify which subset of predictors should be included into the model. Hence, the main thrust of this dissertation ...
Topics in imbalanced data classification : AdaBoost and Bayesian relevance vector machine
(University of Missouri--Columbia, 2020)
This research has three parts addressing classification, especially the imbalanced data problem, which is one of the most popular and essential issues in the domain of classification. The first part is to study the Adaptive ...
Bayesian model averaging for mathematics achievement growth rate trends
(University of Missouri--Columbia, 2022)
In this study, we investigated the use of Bayesian model averaging (BMA) for latent growth curve models. We used the Trends in International Mathematics and Science Study (TIMSS) to predict growth rates in 8th-grade students' ...